Data mining models
1. Data mining in business -- 2. Business data mining tools -- 3. Data mining processes and knowledge discovery -- 4. Overview of data mining techniques -- 5. Data mining software -- 6. Regression algorithms in data mining -- 7. Neural networks in data mining -- 8. Decision tree algorithms -- 9. Sca...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, New York (222 East 46th Street, New York, NY 10017)
Business Expert Press
2018
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| Ausgabe: | Second edition |
| Schriftenreihe: | Big data and business analytics collection
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| Schlagworte: | |
| Online-Zugang: | FHN01 FWS01 FWS02 UBY01 URL des Erstveröffentlichers |
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| Zusammenfassung: | 1. Data mining in business -- 2. Business data mining tools -- 3. Data mining processes and knowledge discovery -- 4. Overview of data mining techniques -- 5. Data mining software -- 6. Regression algorithms in data mining -- 7. Neural networks in data mining -- 8. Decision tree algorithms -- 9. Scalability -- Notes -- References -- Index Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to first describe the benefits of data mining in business, describe the process and typical business applications, describe the workings of basic data mining models, and demonstrate each with widely available free software. This second edition updates Chapter 1, and adds more details on Rattle data mining tools. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We will demonstrate use of R through Rattle. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use. We will demonstrate methods with a small but typical business dataset. We use a larger (but still small) realistic business dataset for Chapter 9 |
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| Beschreibung: | Includes bibliographical references (pages 163-166) and index |
| Beschreibung: | Online-Ressource (170 pages) |
| ISBN: | 9781948580502 9781948580496 |